Tremendous effort has been applied in recent years to increase the accuracy of part-of-speech (POS) tagging. While POS tagging has been extensively applied in text-to-speech and machine translation technologies, not much has been done on utilizing it to improve information retrieval. Information retrieval systems regard a match between words in a query and words in a potential search result document as a positive signal indicative of the document's relevance. Word senses or usages, however, are generally ignored often leading to poor search results. For instance, if a user inputs the query “how rich are GOP candidates” or the query “what do we mean by hypothesis,” where “rich” appears as an adjective and “mean” appears as a verb, respectively, search result documents having “rich” or “mean” utilized as nouns appear as relevant when, in fact, it is likely they are not.